Transactionally derived metadata for refreshing local materialized views with external table sources

As the technological capacity for organizations to create, track, and retain information continues to grow, a variety of different technologies for managing and storing the rising tide of information have been developed. Database systems, for example, provide clients with many different specialized or customized configurations of hardware and software to manage stored information. However, the increasing amounts of data that organizations must store and manage often correspondingly increases both the size and complexity of data storage and management technologies, like database systems, which in turn escalate the cost of maintaining the information.

New technologies more and more seek to reduce both the complexity and storage requirements of maintaining data while simultaneously improving the efficiency of data processing. For example, data processing resources may be efficiently configured to perform different workloads. However, given that many workloads are unknown when data processing resources are configured, or change over time. Challenges in obtaining the right configuration of data processing resources occur frequently.

While embodiments are described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that embodiments are not limited to the embodiments or drawings described. It should be understood, that the drawings and detailed description thereto are not intended to limit embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include,” “including,” and “includes” mean including, but not limited to.

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the present invention. The first contact and the second contact are both contacts, but they are not the same contact.

Materialized views offer improved database system performance by combining data from one or more sources into a view, such as a table, that can be access more quickly than the underlying sources. Use of materialized can improve the performance of queries, for example, and can be explicitly referenced in a query or automatically used to perform a query even when the query did not specify the materialized views.

As database systems interact with further sources of data, database systems may leverage the storage capabilities of other data storage systems to store data that can then be used or accessed by the database system without using database system resources (e.g., storage capacity) to store the data. For example, some database systems may support the use of external tables, which may be tables that are specified, identified to, or otherwise treated as a table like tables of data that are stored internally as part of a database system, but are actually stored in a separate, external data storage system. These external tables may be sources of materialized views, in some embodiments.

Because materialized views offer performance improvements to queries and/or other database system operations, increasing the usage of materialized views can increase the performance improvements achieved at the database system. Materialized views, however, can grow stale or otherwise become out of date as data in the sources used to create the materialized view may have changed. As a materialized view creates a duplicate of data from one or more sources, techniques to refresh or otherwise bring a materialized view up to date may be performed. Such techniques, however, may have to account for scenarios when external tables are sources of the materialized view.

For example, external data storage systems may store data of external tables in various file formats which may or may not be native (e.g., directly accessible) by a database system. These file formats may also support various ways of interacting with the external tables in order to, for example, add, remove or modify data in the external data tables. For example, an external table may be stored as different partitions corresponding to time ranges (e.g., months), locations (e.g., states or territories), or other values of a column in the external table. New partitions may be added, older partitions deleted, or other changes made. These changes may be made transactionally for file formats that are transactional file formats. For instance, transactional file formats may support transactional operations and ensure that data in the external table is accurate, consistent, and supports tracking how data and data structure changes over time. These properties are collectively known as Atomicity, Consistency, Isolation, and Durability (ACID): Atomicity guarantees that each transaction is a single event that either succeeds or fails completely. Consistency ensures that all data written is valid according to the defined rules of the data lake, ensuring that data is accurate and reliable. Isolation ensures multiple transactions can occur at the same time without interfering with each other, ensuring that each transaction executes independently. Durability means that data is not lost or corrupted once a transaction is submitted. Data can be recovered in the event of a system failure, such as a power outage. Some file formats, however, may be non-transactional, which do not support transactional updates to the external table in the storage system of the external table.

Various techniques for transactionally derived metadata for refreshing local materialized views with external table sources are described herein. Such techniques may improve the performance of database systems by efficiently identifying when and how to update materialized views that use external tables as sources. For example, such techniques may be performed independently of the file format used to store the external table in the external data storage system. In this way, the various types of external tables that can be externally stored can be used as sources of a materialized view without degrading database system performance when it is time to refresh the materialized view.

1 FIG. 4 5 FIGS.and 110 110 110 130 120 130 illustrates a logical block diagram of transactionally derived metadata for refreshing local materialized views with external table sources, according to some embodiments. Database systemmay be a standalone database system or a service, such as database service. Database systemmay store and/or provide access to various data sets, including materialized views, like local materialized viewthat may be created for subsequent query access (e.g., as discussed with regard tobelow). Materialized view maintenancemay be implemented in order to ensure that local materialized viewis updated as changes to sources of the materialized view (e.g., other tables are updated).

1 FIG. 150 131 130 150 110 130 131 130 110 As illustrated in, external table(s) stored as one or more data objects (e.g., files, partitions, or other data structures) in external data storage systemmay be sourcesfor local materialized view. This may allow for systemsto act as a data lake, storing many different tables of file formats (which may not be native or supported by database system), but still accessible to make data available via local materialized view. Although external table(s) alone are depicted as source(s)for local materialized view, in some embodiments additional sources, such as local tables within database systemmay also be used along with external tables (e.g., joined as specified in a materialized view definition).

120 140 140 142 130 146 160 148 140 144 5 6 FIGS.and To ensure that materialized view maintenancecan keep local materialized view updated, a metadatamay be maintained. This metadatamay be transactionally derived, as indicated at(as discussed in detail below). In this way, incremental materialized view updates (as opposed to generating local materialized viewagain entirely) may be performed, allowing materialized view maintenance to get materialized view updatesdirectly from specific data objects of external table(s)and then apply the updates. In this way, only those data objects (e.g., files) of an external may need to be scanned or otherwise evaluated to update local materialized view. This can achieve significant performance improvements as some external tables can be very large, and operations to scan entire external tables can be costly in terms of time and resources. For a frequently updated materialized view, such as local materialized view, implementing transactionally derived metadata for refreshing local materialized views with external table sources. As discussed in detail below with regard to, transactionally derived updates to metadatamay allow for derived metadata to indicate when updatesto metadata are made (e.g., by evaluating transaction identifiers or other provenance information that tracks or describes a history changes to an external table's data objects).

170 140 170 150 Different metadata sourcesmay be used for derived metadata. For example, some file formats, such as transactional file formats, may maintain a manifest, or other type of file that describes or includes metadata, including the provenance of data objects (e.g., files) of an external table when changes are made. Another sourcemay be information obtained from external data storage system, such as directory and/or file information that describes, for example, when data objects are created, updated, and/or other provenance information for the data objects.

Please note that the previous description is a logical description and thus is not to be construed as limiting as to the implementation of a these features, or portions thereof.

This specification continues with a general description of a provider network that implements multiple different services, including a database service and storage service, which may transactionally derived metadata for refreshing local materialized views with external table sources for databases managed by the database service. Then various examples of the database service and storage service, including different components/modules, or arrangements of components/module that may be employed as part of implementing the services are discussed. A number of different methods and techniques to implement s transactionally derived metadata for refreshing local materialized views with external table sources for databases managed by the database service are then discussed, some of which are illustrated in accompanying flowcharts. Finally, a description of an example computing system upon which the various components, modules, systems, devices, and/or nodes may be implemented is provided. Various examples are provided throughout the specification.

2 FIG. 200 250 is a logical block diagram illustrating a provider network offering a database service that implements transactionally derived metadata for refreshing local materialized views with external table sources for databases managed by the database service, according to some embodiments. Provider networkmay be a private or closed system or may be set up by an entity such as a company or a public sector organization to provide one or more services (such as various types of cloud-based storage) accessible via the Internet and/or other networks to clients.

200 1000 200 200 200 8 FIG. Provider networkmay be implemented in a single location or may include numerous data centers hosting various resource pools, such as collections of physical and/or virtualized computer servers, storage devices, networking equipment and the like (e.g., computing systemdescribed below with regard to), needed to implement and distribute the infrastructure and storage services offered by the provider network. The provider networkcan be formed as a number of regions, where a region is a separate geographical area in which the cloud provider clusters data centers. Each region can include two or more availability zones connected to one another via a private high speed network, for example a fiber communication connection. An availability zone (also known as an availability domain, or simply a “zone”) refers to an isolated failure domain including one or more data center facilities with separate power, separate networking, and separate cooling from those in another availability zone. Preferably, availability zones within a region are positioned far enough away from one other that the same natural disaster should not take more than one availability zone offline at the same time. Database users can connect to availability zones of the provider networkvia a publicly accessible network (e.g., the Internet, a cellular communication network).

200 200 Regions are connected to a global network which includes private networking infrastructure (e.g., fiber connections controlled by the cloud provider) connecting each region to at least one other region. The provider networkmay deliver content from points of presence outside of, but networked with, these regions by way of edge locations and regional edge cache servers. An edge location can be an extension of the cloud provider network outside of the traditional region/AZ context. For example an edge location can be a data center positioned to provide capacity to a set of database users within a certain latency requirement, a set of servers provided to a database user's premises, or a set of servers provided within (or forming part of) a cellular communications network, each of which can be controlled at least in part by the control plane of a nearby AZ or region. This compartmentalization and geographic distribution of computing hardware enables the provider networkto provide low-latency resource access to database users on a global scale with a high degree of fault tolerance and stability.

The traffic and operations of the provider network may broadly be subdivided into two categories in various embodiments: control plane operations carried over a logical control plane and data plane operations carried over a logical data plane. While the data plane represents the movement of user data through the distributed computing system, the control plane represents the movement of control signals through the distributed computing system. The control plane generally includes one or more control plane components distributed across and implemented by one or more control servers. Control plane traffic generally includes administrative operations, such as system configuration and management (e.g., resource placement, hardware capacity management, diagnostic monitoring, system state information). The data plane includes database user resources that are implemented on the cloud provider network (e.g., computing instances, containers, block storage volumes, databases, file storage). Data plane traffic generally includes non-administrative operations such as transferring database user data to and from the database user resources. Certain control plane components (e.g., tier one control plane components such as the control plane for a virtualized computing service) are typically implemented on a separate set of servers from the data plane servers, while other control plane components (e.g., tier two control plane components such as analytics services) may share the virtualized servers with the data plane, and control plane traffic and data plane traffic may be sent over separate/distinct networks.

200 210 270 In some embodiments, provider networkmay implement various computing resources or services, such as database service(s), (e.g., relational database services, non-relational database services, graph database, time series database, a map reduce service, a data warehouse service, and/or other large scale data processing services or various other types database services), data storage service(e.g., object storage services or block-based storage services that may implement a centralized data store for various types of data), and/or any other type of network based services (which may include a virtual compute service and various other types of storage, processing, analysis, communication, event handling, visualization, and security services not illustrated).

2 FIG. 2 FIG. 8 FIG. 210 270 In various embodiments, the components illustrated inmay be implemented directly within computer hardware, as instructions directly or indirectly executable by computer hardware (e.g., a microprocessor or computer system), or using a combination of these techniques. For example, the components ofmay be implemented by a system that includes a number of computing nodes (or simply, nodes), each of which may be similar to the computer system embodiment illustrated inand described below. In various embodiments, the functionality of a given system or service component (e.g., a component of database serviceor data storage service) may be implemented by a particular node or may be distributed across several nodes. In some embodiments, a given node may implement the functionality of more than one service system component (e.g., more than one data store component).

210 210 270 210 210 232 3 7 FIGS.- Database servicesmay be (or included in) various types of data processing services that perform general or specialized data processing functions (e.g., anomaly detection, machine learning, data mining, big data querying, or any other type of data processing operation). For example, in at least some embodiments, database servicesmay include a map reduce service that creates clusters of processing nodes that implement map reduce functionality over data stored in the map reduce cluster as well as data stored in data storage service. In another example, database servicemay include various types of database services (both relational and non-relational) for storing, querying, and updating data. Such services may be enterprise-class database systems that are highly scalable and extensible. Queries may be directed to a database in database servicethat is distributed across multiple physical resources, and the resource configurations, such as processing clusters, used to process the queries may be scaled up or down on an as needed basis, as discussed in detail below with regard to.

210 210 270 200 Database servicemay work effectively with database schemas of various types and/or organizations, in different embodiments. In some embodiments, clients/subscribers may submit queries in a number of ways, e.g., interactively via an SQL interface to the database system. In other embodiments, external applications and programs may submit queries using Open Database Connectivity (ODBC) and/or Java Database Connectivity (JDBC) driver interfaces to the database system. For instance, database servicemay implement, in some embodiments, a data warehouse service, that utilizes another data processing service, to execute portions of queries or other access requests with respect to data that is stored in a remote data store, such as data storage service(s)(or a data store external to provider network) to implement distributed data processing for distributed data sets.

2210 210 220 232 210 210 In at least some embodiments, database servicemay be a data warehouse service. Thus in the description that follows database servicemay be discussed according to the various features or components that may be implemented as part of a data warehouse service, including control plane, and processing clusters. Note that such features or components may also be implemented in a similar fashion for other types of database services and thus the following examples may be applicable to other types of database service. Database servicemay implement one (or more) processing clusters that are attached to a database (e.g., a data warehouse). In some embodiments, these processing clusters may be designated as a primary and secondary (or concurrent, additional, or burst processing clusters) that perform queries to an attached database warehouse.

210 In embodiments where database serviceis a data warehouse service, the data warehouse service may offer clients a variety of different data management services, according to their various needs. In some cases, clients may wish to store and maintain large of amounts data, such as sales records marketing, management reporting, business process management, budget forecasting, financial reporting, website analytics, or many other types or kinds of data. A client's use for the data may also affect the configuration of the data management system used to store the data. For instance, for certain types of data analysis and other operations, such as those that aggregate large sets of data from small numbers of columns within each row, a columnar database table may provide more efficient performance. In other words, column information from database tables may be stored into data blocks on disk, rather than storing entire rows of columns in each data block (as in traditional database schemes). The following discussion describes various embodiments of a relational columnar database system. However, various versions of the components discussed below as may be equally adapted to implement embodiments for various other types of relational database systems, such as row-oriented database systems. Therefore, the following examples are not intended to be limiting as to various other types or formats of database systems.

In some embodiments, storing table data in such a columnar fashion may reduce the overall disk I/O requirements for various queries and may improve analytic query performance. For example, storing database table information in a columnar fashion may reduce the number of disk I/O requests performed when retrieving data into memory to perform database operations as part of processing a query (e.g., when retrieving all of the column field values for all of the rows in a table) and may reduce the amount of data that needs to be loaded from disk when processing a query. Conversely, for a given number of disk requests, more column field values for rows may be retrieved than is necessary when processing a query if each data block stored entire table rows. In some embodiments, the disk requirements may be further reduced using compression methods that are matched to the columnar storage data type. For example, since each block contains uniform data (i.e., column field values that are all of the same data type), disk storage and retrieval requirements may be further reduced by applying a compression method that is best suited to the particular column data type. In some embodiments, the savings in space for storing data blocks containing only field values of a single column on disk may translate into savings in space when retrieving and then storing that data in system memory (e.g., when analyzing or otherwise processing the retrieved data).

210 1000 220 220 232 232 232 220 220 232 210 220 210 8 FIG. a b c Database servicemay be implemented by a large collection of computing devices, such as customized or off-the-shelf computing systems, servers, or any other combination of computing systems or devices, such as the various types of systemsdescribed below with regard to. Different subsets of these computing devices may be controlled by control plane. Control plane, for example, may provide a cluster control interface to clients or users who wish to interact with the processing clusters, such as processing cluster(s),, andmanaged by control plane. For example, control planemay generate one or more graphical user interfaces (GUIs) for clients, which may then be utilized to select various control functions offered by the control interface for the processing clustershosted in the database service. Control planemay provide or implement access to various metrics collected for the performance of different features of database service, including processing cluster performance, in some embodiments.

210 232 3 5 FIGS.and As discussed above, various clients (or database users, organizations, entities, or users) may wish to store and manage data using a database service. Processing clustersmay respond to various requests, including write/update/store requests (e.g., to write data into storage) or queries for data (e.g., such as a Server Query Language request (SQL) for particular data), as discussed below with regard to. For example, multiple users or clients may access a processing cluster to obtain data warehouse services.

210 232 For databases manually managed by users, database servicemay provide database endpoints directly to the clusters which allow the users manage in order to implement client applications that send requests and other messages directly to a particular cluster. Database endpoints, for example may be a network endpoint associated with a particular network address, such as a URL, which points to a resources, such as processing clustersthat are attached to the database for query processing. For instance, a client may be given the network endpoint “http://mycluster.com” to send various request messages to. Multiple clients (or users of a particular client) may be given a database endpoint for the same database. Various security features may be implemented to prevent unauthorized users from accessing the databases.

232 232 232 210 232 232 270 200 272 274 232 270 232 23 a b c 5 FIG. Processing clusters, such as processing clusters,, and, hosted by database servicemay provide an enterprise-class database query and management system that allows users to send data processing requests to be executed by the clusters, such as by sending a query. Processing clustersmay perform data processing operations with respect to data stored locally in a processing cluster, as well as remotely stored data. For example, data storage serviceimplemented by provider networkthat stores remote data, such as backups or other data of a database stored in a cluster. In some embodiments, external table data objectsand, in some scenarios external table metadata, may not be stored locally in a processing clusterbut instead may be stored in data storage service(e.g., with data being partially or temporarily stored in processing clusterto perform queries). Queries sent to a processing cluster(or routed/redirect/assigned/allocated to processing cluster(s)) may be directed to local data stored in the processing cluster and/or remote data. Therefore, processing clusters may implement local data processing, such as local data processing, (discussed below with regard to) to plan and execute the performance of queries with respect to local data in the processing cluster, as well as a remote data processing client.

210 230 230 230 230 232 230 232 232 a b c Database servicemay implement different types or configurations of processing clusters. For example, different configurations A, B, and C, may utilize various different configurations of computing resources, including, but not limited to, different numbers of computational nodes, different processing capabilities (e.g., processor size, power, custom or task-specific hardware, such as hardware optimized to perform different operations, such as regular expression searching, or other data processing operations), different amounts of memory, different networking capabilities, and so on. Thus, for some queries, different configurationsof processing clustermay offer different execution times. Different configurationsof processing clustersmay be maintained in different pools of available processing clusters to be attached to a database. Attached processing clusters may then be made exclusively assigned or allocated for the use of performing queries to the attached database, in some embodiments. The number of processing clustersattached to a database may change over time according to the selection techniques discussed below.

210 330 220 In some embodiments, database servicemay have at least one processing cluster attached to a database, which may be the “primary cluster” or “main cluster.” Primary clustersmay be reserved, allocated, permanent, or otherwise dedicated processing resources that store and/or provide access to a database for a client, in some embodiments. Primary clusters, however, may be changed. Techniques to resize or change to a different configuration of a primary cluster may be performed, in some embodiments. The available processing clusters that may also be attached, as determined, to a database may be maintained (as noted earlier) in different configuration type pools, which may be a set of warmed, pre-configured, initialized, or otherwise prepared clusters which may be on standby to provide additional query performance capacity for a primary cluster. Control planemay manage cluster pools by managing the size of cluster pools (e.g., by adding or removing processing clusters based on demand).

210 250 272 270 272 272 272 272 232 As databases are created, updated, and/or otherwise modified, snapshots, copies, or other replicas of the database at different states may be stored separate from database servicein data storage service, in some embodiments. For example, a leader node, or other processing cluster component, may implement a backup agent or system that creates and store database backups for a database to be stored as database datain data storage service. Database datamay include user data (e.g., tables, rows, column values, etc.) and database metadata (e.g., information describing the tables which may be used to perform queries to a database, such as schema information, data distribution, range values or other content descriptors for filtering out portions of a table from a query, etc.). A timestamp or other sequence value indicating the version of database datamay be maintained in some embodiments, so that the latest database datamay, for instance, be obtained by a processing cluster in order to perform queries. In at least some embodiments, database datamay be treated as the authoritative version of data, and data stored in processing clustersfor local processing as a cached version of data.

270 250 250 270 270 Data storage servicemay implement different types of data stores for storing, accessing, and managing data on behalf of clientsas a network-based service that enables clientsto operate a data storage system in a cloud or network computing environment. Data storage service(s)may also include various kinds of object or file data stores for putting, updating, and getting data objects or files. For example, one data storage servicemay be an object-based data store that allows for different data objects of different formats or types of data, such as structured data (e.g., database data stored in different database schemas), unstructured data (e.g., different types of documents or media content), or semi-structured data (e.g., different log files, human-readable data in different formats like JavaScript Object Notation (JSON) or Extensible Markup Language (XML)) to be stored and managed according to a key value or other unique identifier that identifies the object.

270 270 270 220 In at least some embodiments, data storage service(s)may be treated as a data lake. For example, an organization may generate many different kinds of data, stored in one or multiple collections of data objects in a data storage service. The data objects in the collection may include related or homogenous data objects, such as database partitions of sales data, as well as unrelated or heterogeneous data objects, such as audio files and web site log files. Data storage service(s)may be accessed via programmatic interfaces (e.g., APIs) or graphical user interfaces. For example, format independent data processing servicemay access data objects stored in data storage services via the programmatic interfaces.

250 200 260 210 270 250 250 210 270 250 200 250 Generally speaking, clientsmay encompass any type of client that can submit network-based requests to provider networkvia network, including requests for storage services (e.g., a request to query a database service, or a request to create, read, write, obtain, or modify data in data storage service(s), etc.). For example, a given clientmay include a suitable version of a web browser, or may include a plug-in module or other type of code module that can execute as an extension to or within an execution environment provided by a web browser. Alternatively, a clientmay encompass an application such as a database application (or user interface thereof), a media application, an office application or any other application that may make use of database service(s)or storage resources in data storage service(s)to store and/or access the data to implement various applications. In some embodiments, such an application may include sufficient protocol support (e.g., for a suitable version of Hypertext Transfer Protocol (HTTP)) for generating and processing network-based services requests without necessarily implementing full browser support for all types of network-based data. That is, clientmay be an application that can interact directly with provider network. In some embodiments, clientmay generate network-based services requests according to a Representational State Transfer (REST)-style network-based services architecture, a document- or message-based network-based services architecture, or another suitable network-based services architecture.

250 200 250 270 270 250 250 210 In some embodiments, a clientmay provide access to provider networkto other applications in a manner that is transparent to those applications. For example, clientmay integrate with an operating system or file system to provide storage on one of data storage service(s)(e.g., a block-based storage service). However, the operating system or file system may present a different storage interface to applications, such as a conventional file system hierarchy of files, directories and/or folders. In such an embodiment, applications may not need to be modified to make use of the storage system service model. Instead, the details of interfacing to the data storage service(s)may be coordinated by clientand the operating system or file system on behalf of applications executing within the operating system environment. Similarly, a clientmay be an analytics application that relies upon data processing service(s)to execute various queries for data already ingested or stored in the data processing service (e.g., such as data maintained in a data warehouse service).

250 270 210 200 260 260 250 200 260 260 250 200 260 250 200 250 200 210 220 270 200 210 250 Clientsmay convey network-based services requests (e.g., access requests to read or write data may be directed to data in data storage service(s), or operations, tasks, or jobs, such as queries, being performed as part of data processing service(s)) to and receive responses from provider networkvia network. In various embodiments, networkmay encompass any suitable combination of networking hardware and protocols necessary to establish network-based-based communications between clientsand provider network. For example, networkmay generally encompass the various telecommunications networks and service providers that collectively implement the Internet. Networkmay also include private networks such as local area networks (LANs) or wide area networks (WANs) as well as public or private wireless networks. For example, both a given clientand provider networkmay be respectively provisioned within enterprises having their own internal networks. In such an embodiment, networkmay include the hardware (e.g., modems, routers, switches, load balancers, proxy servers, etc.) and software (e.g., protocol stacks, accounting software, firewall/security software, etc.) necessary to establish a networking link between given clientand the Internet as well as between the Internet and provider network. It is noted that in some embodiments, clientsmay communicate with provider networkusing a private network rather than the public Internet. In some embodiments, clients of data processing services, format independent data processing service, and/or data storage service(s)may be implemented within provider network(e.g., an application hosted on a virtual computing resource that utilizes a data processing serviceto perform database queries) to implement various application features or functions and thus various features of client(s)discussed above may be applicable to such internal clients as well.

3 FIG. 2 FIG. 300 232 300 310 320 320 320 310 312 314 300 317 300 322 322 322 a b n a b n is a logical block diagram illustrating an example processing cluster of a database service, according to some embodiments. Processing clustermay be query processing cluster, like processing clustersdiscussed above with regard to, that distributes execution of a query among multiple computational (which may also be referred to as “compute”) nodes. As illustrated in this example, a processing clustermay include a leader nodeand compute nodes,, and, which may communicate with each other over an interconnect (not illustrated). Leader nodemay implement query planningto generate query plan(s), query executionfor executing queries on processing clusterthat perform data processing that can utilize remote query processing resources for remotely stored data (e.g., by utilizing one or more query execution slot(s)/queue(s)). As described herein, each node in a processing clustermay include attached storage, such as attached storage,, and, on which a database (or portions thereof) may be stored on behalf of clients (e.g., users, client applications, and/or storage service subscribers).

300 300 300 3 FIG. Note that in at least some embodiments, query processing capability may be separated from compute nodes, and thus in some embodiments, additional components may be implemented for processing queries. Additionally, it may be that in some embodiments, no one node in processing clusteris a leader node as illustrated in, but rather different nodes of the nodes in processing clustermay act as a leader node or otherwise direct processing of queries to data stored in processing cluster. While nodes of processing cluster may be implemented on separate systems or devices, in at least some embodiments, some or all of processing cluster may be implemented as separate virtual nodes or instance on the same underlying hardware system (e.g., on a same server).

310 250 310 301 303 240 310 310 2 FIG. Leader nodemay manage communications with clients, such as clientsdiscussed above with regard to. For example, leader nodemay communicate with a proxy service and may receive queryand return query resultsto proxy service(instead of communicating directly with a client application). Alternatively, in those embodiments where leader nodeimplements database query queue and query routing as a primary cluster, then leader nodemay act as the proxy for other, secondary clusters, attached to the database, and may return query results directly to a client application.

310 301 310 310 301 300 320 310 320 300 304 314 310 320 301 301 310 310 320 303 301 Leader nodemay be a node that receives a queryfrom various client programs (e.g., applications) and/or subscribers (users) (either directly or routed to leader nodefrom a proxy service), then parses them and develops an execution plan (e.g., query plan(s)) to carry out the associated database operation(s)). More specifically, leader nodemay develop the series of steps necessary to obtain results for the query. Querymay be directed to data that is stored both locally within processing cluster(e.g., at one or more of compute nodes) and data stored remotely. Leader nodemay also manage the communications among compute nodesinstructed to carry out database operations for data stored in the processing cluster. For example, node-specific query instructionsmay be generated or compiled code by query executionthat is distributed by leader nodeto various ones of the compute nodesto carry out the steps needed to perform query, including executing the code to generate intermediate results of queryat individual compute nodes may be sent back to the leader node. Leader nodemay receive data and query responses or results from compute nodesin order to determine a final resultfor query.

310 300 313 300 311 301 303 3 FIG. 5 FIG. A database schema, data format and/or other metadata information for the data stored among the compute nodes, such as the data tables stored in the cluster, may be managed and stored by leader node. Query planning may account for remotely stored data by generating node-specific query instructions that include remote operations to be directed by individual compute node(s). As illustrated in, processing clustercould be a “primary” cluster that may send requests to an additional cluster for the database, as indicated atin order to scale query performance (as discussed below with regard to). Alternatively, if processing clusterwere a “secondary” processing cluster, then as illustrated at, additional requests may be received from the additional cluster (instead of queryand query results).

300 320 320 320 320 1000 324 324 324 304 324 320 324 322 322 322 318 318 318 324 322 320 a b n a b n a b n a b n 8 FIG. Processing clustermay also include compute nodes, such as compute nodes,, and. Compute nodes, may for example, be implemented on servers or other computing devices, such as those described below with regard to computer systemin, and each may include individual query processing “slices” defined, for example, for each core of a server's multi-core processor, one or more query processing engine(s), such as query engine(s),, and, to execute the instructionsor otherwise perform the portions of the query plan assigned to the compute node. Query engine(s)may access a certain memory and disk space in order to process a portion of the workload for a query (or other database operation) that is sent to one or more of the compute nodes. Query enginemay access attached storage, such as,, and, to perform local operation(s), such as local operations,, and. For example, query enginemay scan data in attached storage, access indexes, perform joins, semi joins, aggregations, or any other processing operation assigned to the compute node.

324 316 316 316 326 326 326 326 220 332 332 332 326 334 334 334 340 270 318 a a b n a b n a b n a b c Query enginemay also direct the execution of remote data processing operations, by providing remote operation(s), such as remote operations,, and, to remote data processing clients, such as remote data processing client,, and. Remote data processing clientsmay be implemented by a client library, plugin, driver or other component that sends request sub-queries to be performed by data storage serviceor requests to for data,,, and. As noted above, in some embodiments, Remote data processing clientsmay read, process, or otherwise obtain data,, and, in response from database datain data storage service, which may further process, combine, and or include them with results of location operations.

320 310 326 332 Compute nodesmay send intermediate results from queries back to leader nodefor final result generation (e.g., combining, aggregating, modifying, joining, etc.). Remote data processing clientsmay retry data requeststhat do not return within a retry threshold.

322 Attached storagemay be implemented as one or more of any type of storage devices and/or storage system suitable for storing data accessible to the compute nodes, including, but not limited to: redundant array of inexpensive disks (RAID) devices, disk drives (e.g., hard disk drives or solid state drives) or arrays of disk drives such as Just a Bunch Of Disks (JBOD), (used to refer to disks that are not implemented according to RAID), optical storage devices, tape drives, RAM disks, Storage Area Network (SAN), Network Access Storage (NAS), or combinations thereof. In various embodiments, disks may be formatted to store database tables (e.g., in column oriented data formats or other data formats).

4 FIG. 3 FIG. 410 310 510 401 410 411 422 420 is a logical block diagram illustrating a request to create a local materialized view from external table sources, according to some embodiments. Processing cluster, similar to processing clusterandbelow, may receive a request to create a materialized view from external table sources, as indicated at. For example, the request may identify the external tables in a materialized view definition (e.g., in a query language, such as SQL) which may select, filter, join, operate upon (e.g., aggregate functions, such as sum or average) data into local materialized view. Processing clustermay execute the materialized view definition, as indicated at, in order to access external table data object(s)in external data storage system(e.g., using remote operation as discussed above with regard to).

412 414 414 413 415 420 414 422 In addition creating local materialized view, processing cluster may create metadata. In some embodiments, metadatamay be stored as a table similar to other user data tables, such as local materialized view or other tables, but hidden or managed as a system table. To create metadata, as indicated atand, one or more sources in external data storage systemmay be accessed in order to create the corresponding rows or entries in a local table that include various information such as, access information, including file paths or other storage locations, names or descriptions of the contents, transaction identifiers, among other information. For example, an entry may identify when a data object was created, another entry may identify if the data object was deleted, and so on. In this way, metadatamay maintain the provenance of each of external table(s) data object(s).

424 426 426 420 414 File format managed metadatamay include the information that may be maintained or updated according to the file format of the external data objects, such as transactional file format. Other information may be accessed in addition to or instead of (e.g., if file format managed metadata does not exist because of a non-transactional file format being used), such as storage system data object metadata. Storage system data object metadatamay include metadata maintained by external data storage system, which can be used to derive or otherwise create metadata.

For example, consider metadata of an external table that stores different geographic data partitioned according to region (e.g., Europe, America, Asia). The metadata that may be stored in the local table could be:

TABLE 1 oid insertxid deletexid partition_1 partition_ . . . $path $etag $size 1 1000 922337 Europe . . . U://europe/file_1 3sL4k 2048 2 1000 922337 America . . . U://america/file_1 pXroD 1024 3 1001 922337 Europe . . . U://europe/file_2 qtJlc  256

In this example, columns oid, insertxid and deletexid may be system attributes for the external tables. Partition columns may be part of the metadata and represented in relational form. Each pair ($path, $etag) pay be a pointer to a data file in the external storage containing tuples that are conceptually nested. Specifically, $etag can encode the object's ETag and can be expected to be present only for some file formats. The $etag column may have different semantics for other table formats. Column $size may be used to parallelize scans of external files, in some embodiments.

Transactional table formats may already encode system attributes oid, insertxid and deletexid. For non-transactional file formats, they can be defined when taking snapshots on the external table. Operations on partitions or data files can be replicated as local transactions. For example, the newly observed snapshot (xid 1002) could have removed a partition along with its data file (oid 2) and added some other partition or data file, such as:

oid insertxid deletexid partition_1 partition _ . . . $path $etag $size 1 1000 922337 Europe . . . U://europe/file_1 3sL4k 2048 2 1000  1002 America . . . U://america/file_1 pXroD 1024 3 1001 922337 Europe . . . U://europe/file_2 qtJlc  256 4 1002 922337 Asia . . . U://asia/file_1 x = 7D5  512

5 FIG. 6 7 FIGS.and 510 560 570 501 520 560 501 501 520 560 502 530 560 509 is a logical block diagram illustrating interactions for different updates to local metadata and local materialized views, according to some embodiments. Processing clustermay have a materialized viewwhich is sourced from external tables (and possibly local tables, not illustrated) described by metadata. Different scenarios may cause materialized view updates, as discussed below with regard to. For example, a querymay be received. As part of processing, auto query rewritemay identify materialized viewwhich can be used to improve performance of query(e.g., by accessing data used to generate the result of query). However, auto query rewritemay also determine that materialized viewis stale, requesting a refresh, as indicated at. In another example, auto MV refreshmay evaluate various materialized views, including materialized viewto determine that it should be refreshed (e.g., based on a latest transaction identifier and send a refresh request.

503 540 506 561 560 507 504 505 508 507 A manual request to refreshmay also be received. Refresh executionmay handle performing a refresh operation, by getting external table dataand making corresponding updatesto materialized view. However, first updates to metadata may be determinedin order to indicate the correct data to obtain. These updates may be obtained by various techniques, including polling, as indicated ator receiving notifications. Updates, as indicatedmay be made so that metadatacan be used for query execution and subsequent refresh operations.

504 570 505 550 7 FIG. Polling techniquesfor metadata may include sending periodic requests to external data storage systems for an external table in order to ascertain whether metadatashould be updated. Different techniques for making these requests are discussed in detail below with regard to, such as using access requests to obtain a snapshot of metadata information or to perform a delta query. A notification of updatesmay include techniques where a listener system or device (not illustrated) may receive events or other notifications of when a change to an external table is made (e.g., a file is added). Then, the notification may be forwarded to metadata replication, which may perform techniques to obtain the update to the metadata (if not provided by the notification).

2 5 FIGS.- 2 5 FIGS.- 2 5 FIGS.- Althoughhave been described and illustrated in the context of a provider network implementing a database service, like a data warehousing service, the various components illustrated and described inmay be easily applied to other database services that can utilize transactionally derived metadata for refreshing local materialized views with external table sources. As such,are not intended to be limiting as to other embodiments of transactionally derived metadata for refreshing local materialized views with external table sources.

6 FIG. is a high-level flowchart illustrating methods and techniques to implement transactionally derived metadata for refreshing local materialized views with external table sources, according to some embodiments. Various different data processing systems and devices may implement the various methods and techniques described below, either singly or working together. For instance, different types of database systems may implement these techniques. Alternatively, various other combinations of different systems and devices located within or without provider networks may implement the below techniques. Therefore, the above examples and or any other systems or devices referenced as performing the illustrated method, are not intended to be limiting as to other different components, systems, or devices.

610 7 FIG. As indicated at, a database system may identify an update to metadata that is transactionally derived for one or more external tables stored in an external data storage system, in some embodiments. The external table(s) may be a source for a local materialized view maintained by the database system. In some embodiments, the identification may be triggered, caused, or otherwise performed in response to a detected refresh event, as discussed below with regard to. In some embodiments, the identification may be performed by periodically checking for updates to the local copy of the metadata.

620 As indicated at, a determination may be made as to which storage objects storing respective portions of the external table(s) in the external data storage system to access according to the identified update to the local copy of the metadata, in some embodiments. For example, the local copy of the metadata may identify various data files, partitions, or other storage structures to access, as rows or entries corresponding to different transactions performed to update the metadata, in some embodiments, providing the provenance of the files. File paths, object identifiers, credentials, and/or any other information that may be used by database system to access the portion(s) of the external table(s) may be included in the metadata, in some embodiments.

630 As indicated at, the local materialized view maintained by the database may be updated according to the accessed portion(s) of the external table(s), in some embodiments. For example, the portions may include new rows that have been added or values in existing rows that have been modified. In some embodiments, the accessed portions may indicate that rows have been deleted (e.g., by tombstone or other markers for deletion). Corresponding updates to the local materialized view may be made. For example, a materialized view definition (e.g., specified as query in a query language, such as SQL) may be used to evaluate the accessed portions to regenerate the materialized view. In at least some embodiments, delta or incremental maintenance operations may be performed so that only portions of the local materialized view that need to be changed (e.g., to add/modify/remove values) are updated.

7 FIG. 710 is a high-level flowchart illustrating methods and techniques to implement performing transactional replication to update a local copy of metadata for external tables of a materialized view, according to some embodiments. As indicated at, a refresh event for a local materialized view may be detected, in some embodiments. For example, the refresh event may be a request received (e.g., from a client) to refresh a materialized view, in some embodiments. A refresh event may be triggered by a background process that evaluates existing local materialized views for staleness and instructs/requests stale materialized views to be refreshed, in some embodiments. A refresh event may be triggered by query that specifies the materialized view, or is rewritten to make use of the materialized view, in some embodiments.

720 As indicated at, an access request may be performed to metadata for the external table source(s) to an external data storage system that stores the external table(s) and the metadata, in some embodiments. For example, the access request may be a request to obtain a latest snapshot of the metadata from the external data storage system. In other embodiments, the request may be a delta query that includes, as a predicate or feature of the query, a latest transaction identifier for the local copy of the metadata in order to return only those changes to the metadata that occur after the latest transaction identifier.

730 As indicated at, a determination may be made based on a result of the access request, of the differences between a local copy of the metadata and the metadata in the external data storage system, in some embodiments. For example, the results of the delta query may provide the differences as they may be later than a latest transaction identifier for the local copy of the metadata. For an access request that returns the latest snapshot, a comparison of transaction identifiers (or other changes if the local copy of the metadata stores the metadata in a different format) may be performed to determine the difference(s).

If there are difference(s), then transaction(s) on the local copy of the metadata to replicate the difference(s), in some embodiments. For example, requests to insert new rows, modify existing rows, or delete existing rows, may be performed as a transaction, using transaction semantics to ensure that they are performed correctly and appropriately exposed to readers of the local copy of the metadata (e.g., uncommitted transactions should not be made visible to readers of the local copy of the metadata). Transaction identifiers may be included as part of the entries for subsequent evaluation according to the various techniques discussed above.

8 FIG. 1000 Embodiments of transactionally derived metadata for refreshing local materialized views with external table sources as described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system is illustrated by. In different embodiments, computer systemmay be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing node, compute node, computing device, compute device, or electronic device.

1000 1010 1020 1030 1000 1040 1030 1050 1060 1070 1080 1080 1050 1000 1000 1000 In the illustrated embodiment, computer systemincludes one or more processorscoupled to a system memoryvia an input/output (I/O) interface. Computer systemfurther includes a network interfacecoupled to I/O interface, and one or more input/output devices, such as cursor control device, keyboard, and display(s). Display(s)may include standard computer monitor(s) and/or other display systems, technologies or devices. In at least some implementations, the input/output devicesmay also include a touch- or multi-touch enabled device such as a pad or tablet via which a user enters input via a stylus-type device and/or one or more digits. In some embodiments, it is contemplated that embodiments may be implemented using a single instance of computer system, while in other embodiments multiple such systems, or multiple nodes making up computer system, may host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer systemthat are distinct from those nodes implementing other elements.

1000 1010 1010 1010 1010 1010 In various embodiments, computer systemmay be a uniprocessor system including one processor, or a multiprocessor system including several processors(e.g., two, four, eight, or another suitable number). Processorsmay be any suitable processor capable of executing instructions. For example, in various embodiments, processorsmay be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processorsmay commonly, but not necessarily, implement the same ISA.

1010 In some embodiments, at least one processormay be a graphics processing unit. A graphics processing unit or GPU may be considered a dedicated graphics-rendering device for a personal computer, workstation, game console or other computing or electronic device. Modern GPUs may be very efficient at manipulating and displaying computer graphics, and their highly parallel structure may make them more effective than typical CPUs for a range of complex graphical algorithms. For example, a graphics processor may implement a number of graphics primitive operations in a way that makes executing them much faster than drawing directly to the screen with a host central processing unit (CPU). In various embodiments, graphics rendering may, at least in part, be implemented by program instructions that execute on one of, or parallel execution on two or more of, such GPUs. The GPU(s) may implement one or more application programmer interfaces (APIs) that permit programmers to invoke the functionality of the GPU(s). Suitable GPUs may be commercially available from vendors such as NVIDIA Corporation, ATI Technologies (AMD), and others.

1020 1010 1020 1020 1025 1035 1020 1000 1000 1030 1040 System memorymay store program instructions and/or data accessible by processor. In various embodiments, system memorymay be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing desired functions, such as those described above are shown stored within system memoryas program instructionsand data storage, respectively. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memoryor computer system. Generally speaking, a non-transitory, computer-readable storage medium may include storage media or memory media such as magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computer systemvia I/O interface. Program instructions and data stored via a computer-readable medium may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface.

1030 1010 1020 1040 1050 1030 1020 1010 1030 1030 1030 1020 1010 In one embodiment, I/O interfacemay coordinate I/O traffic between processor, system memory, and any peripheral devices in the device, including network interfaceor other peripheral interfaces, such as input/output devices. In some embodiments, I/O interfacemay perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory) into a format suitable for use by another component (e.g., processor). In some embodiments, I/O interfacemay include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interfacemay be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I/O interface, such as an interface to system memory, may be incorporated directly into processor.

1040 1000 1000 1040 Network interfacemay allow data to be exchanged between computer systemand other devices attached to a network, such as other computer systems, or between nodes of computer system. In various embodiments, network interfacemay support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and/or protocol.

1050 1000 1050 1000 1000 1000 1000 1040 Input/output devicesmay, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system. Multiple input/output devicesmay be present in computer systemor may be distributed on various nodes of computer system. In some embodiments, similar input/output devices may be separate from computer systemand may interact with one or more nodes of computer systemthrough a wired or wireless connection, such as over network interface.

8 FIG. 1020 1025 1035 1025 1025 1035 As shown in, memorymay include program instructions, that implement the various methods and techniques as described herein, and data storage, comprising various data accessible by program instructions. In one embodiment, program instructionsmay include software elements of embodiments as described herein and as illustrated in the Figures. Data storagemay include data that may be used in embodiments. In other embodiments, other or different software elements and data may be included.

1000 1000 Those skilled in the art will appreciate that computer systemis merely illustrative and is not intended to limit the scope of the techniques as described herein. In particular, the computer system and devices may include any combination of hardware or software that can perform the indicated functions, including a computer, personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, network device, internet appliance, PDA, wireless phones, pagers, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device. Computer systemmay also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided and/or other additional functionality may be available.

1000 1000 Those skilled in the art will also appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components may execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a non-transitory, computer-accessible medium separate from computer systemmay be transmitted to computer systemvia transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and/or a wireless link. Various embodiments may further include receiving, sending or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-accessible medium. Accordingly, the present invention may be practiced with other computer system configurations.

It is noted that any of the distributed system embodiments described herein, or any of their components, may be implemented as one or more web services. In some embodiments, a network-based service may be implemented by a software and/or hardware system designed to support interoperable machine-to-machine interaction over a network. A network-based service may have an interface described in a machine-processable format, such as the Web Services Description Language (WSDL). Other systems may interact with the web service in a manner prescribed by the description of the network-based service's interface. For example, the network-based service may define various operations that other systems may invoke, and may define a particular application programming interface (API) to which other systems may be expected to conform when requesting the various operations.

In various embodiments, a network-based service may be requested or invoked through the use of a message that includes parameters and/or data associated with the network-based services request. Such a message may be formatted according to a particular markup language such as Extensible Markup Language (XML), and/or may be encapsulated using a protocol such as Simple Object Access Protocol (SOAP). To perform a web services request, a network-based services client may assemble a message including the request and convey the message to an addressable endpoint (e.g., a Uniform Resource Locator (URL)) corresponding to the web service, using an Internet-based application layer transfer protocol such as Hypertext Transfer Protocol (HTTP).

In some embodiments, web services may be implemented using Representational State Transfer (“RESTful”) techniques rather than message-based techniques. For example, a web service implemented according to a RESTful technique may be invoked through parameters included within an HTTP method such as PUT, GET, or DELETE, rather than encapsulated within a SOAP message.

The various methods as illustrated in the FIGS. and described herein represent example embodiments of methods. The methods may be implemented in software, hardware, or a combination thereof. The order of method may be changed, and various elements may be added, reordered, combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to a person skilled in the art having the benefit of this disclosure. It is intended that the invention embrace all such modifications and changes and, accordingly, the above description to be regarded in an illustrative rather than a restrictive sense.